Physical Digital Face Attack Detection

Physical-digital face attack detection aims to create robust systems that can identify both physically forged (e.g., masks, printed photos) and digitally manipulated (e.g., deepfakes) faces, a crucial step in securing face recognition technology. Current research focuses on developing unified models capable of handling both attack types simultaneously, often employing techniques like Mixture of Experts (MoE) frameworks and vision-language models (VLMs) to learn comprehensive feature spaces and improve generalization. This research area is vital for enhancing the security and trustworthiness of face recognition systems across various applications, from access control to financial transactions.